8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020) Understanding the Involvement of Developers in Missing Link Community Smell: An Exploratory Study on Apache Projects Toukir Ahammed, Moumita Asad and Kazi Sakib Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh Abstract Missing link smell occurs when developers collaborate in source code without communication. This can affect software maintenance by the means of lacking mutual awareness, mistrust and knowledge gap. Existing studies have investigated the relationship of missing link smell with code smell and different socio-technical factors like turnover. This study aims to understand how many developers are involved with missing link smell, by calculating the percentage of smelly developers for a project. The study also investigates the relationship between the number of contributions and the number of missing link involvements of a developer. The result shows that the percentage of smelly developers involved with missing link smell is 8.7% on average. The result also suggests a moderate positive correlation between the contribution of a developer to the project and the involvement in smell. Keywords missing link smell, community smell, software engineering, empirical analysis 1. Introduction The detection of missing link smell and its impact on software artifacts have been analyzed in previous stud- Community smells are the organizational and social anti- ies. S. Magnoni proposed the identification pattern of patterns in a development community [1]. Community missing link community smell [3]. Tamburri et al. ex- smells may lead to the emergence of social debt which amined the relationship between community smells and indicates unforeseen project costs connected to a sub- different socio-technical factors, e.g., socio-technical con- optimal software development community. Community gruence, turnover etc [4]. This study considered missing smells may not be an immediate obstacle for software link, organizational silo, black cloud and radio silence development but these can affect software maintenance community smell. Palomba et al. investigated the impact negatively in the long run [2]. Missing link is one of the of missing link smell and four other community smells on common community smells. It refers to the condition code smell intensity [2]. Catolino et al. analyzed the role when two co-committing developers show uncooperative of four community smells including missing link smell behavior by not communicating [3]. on gender diversity and women participation in open- Missing link community smell decreases communi- source community [5]. However, developer involvement cation activities in the development community. The in missing link smell and how developer contributions in lack of communication and cooperation negatively af- the project relate to missing link smell have not been ana- fects mutual awareness and trust among developers [3]. lyzed yet. In this context, the current study aims to focus A software product can be thought of as the combined ef- on these factors by addressing the following Research fort of all developers. So, collaboration along with proper Questions (RQs). communication is necessary among developers. It is im- RQ1: How many developers are involved in miss- portant to know how many developers are involved in ing link community smell? missing link smell as they may affect the whole project. In an open-source project, there can be many devel- Identifying these developers and analyzing their charac- opers who contribute to the project. All the developers teristics is important. This will help the project managers may not be involved in missing link community smell. to take steps such as task reassigning, team reformation, This RQ aims to find how many developers are involved increasing awareness about communication etc. to keep in missing link smells in a community. This is important communication issues lower among the developers in to know the collective contribution of developers to the the community. number of missing link smells in a project. This finding QuASoQ 2020: 8th International Workshop on Quantitative will help the project managers to understand the severity Approaches to Software Quality of communication issues among developers in the com- email: bsse0806@iit.du.ac.bd (T. Ahammed); bsse0731@iit.du.ac.bd munity. The action can be different to mitigate missing (M. Asad); sakib@iit.du.ac.bd (K. Sakib) link smell based on the number of developers involved Β© 2020 Copyright for this paper by its authors. Use permitted under Creative CEUR Workshop http://ceur-ws.org ISSN 1613-0073 Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org) in smells. Proceedings 64 8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020) RQ2: How does missing link smell relate with a developer contribution? This RQ focuses on the involvement of individual de- veloper in missing link smell. This RQ relates an impor- tant characteristic of a developer, i.e., contribution, to missing link smell. This finding will help project man- agers understanding which type of developers involve more in missing link smell. This information can be used to decide which developers can be monitored to control missing link smell in the community from the beginning of a project. Figure 1: Developer Social Network In this study, missing link smells are analyzed on seven open-source projects of Apache ecosystem. These projects are selected for being large enough to analyse and the in the defined communication channel, i.e., mailing list. availability of communication data, i.e., mailing list. First, Two developers are connected through an edge if they the instances of missing link smell are detected in each replied in the same e-mail within a given time frame [3]. project. The missing link smell is identified by finding A communication network is illustrated in Figure 3. cases where a collaboration does not have its communi- Missing Link Community Smell: A missing link cation counterpart. Then the developers associated with community smell occurs when a couple of developers each smell are identified by extracting the instance of collaborate with each other but show uncooperative be- smell. The fraction of developers involved with missing haviors by not communicating. This smell can be identi- link smell is calculated to check whether a subset of de- fied by detecting collaboration between two developers velopers are involved with this type of smell. Then the that do not have the communication counterpart in de- correlation is investigated between the contribution of fined communication channel, e.g., development mailing developers and their involvement in missing link smells. list [3]. The results of the study show that a small part of the An example of DSN is illustrated in Figure 1. The up- total developers involved with missing link community per part of the graph represents communication and the smell. On average, 8.7% of the total developers of a project lower part represents the collaboration among develop- are involved with missing link smell. This study also finds ers. The developers are connected with a solid line if a significant moderate positive correlation between the they communicate with each other. The developers are developer contribution and their involvement in missing connected to the file icon through a dashed line if they link smell. contribute to that source code file. The collaboration and communication network can be 2. Background generated separately from this DSN. Figure 2 and Fig- ure 3 represent the collaboration and the communication This section provides some important terminologies to network respectively. The missing link smell can be iden- better understand the missing link community smell. tified comparing the collaboration network with the com- Developer Social Network (DSN): A network of a munication network. There is a link between developer software development community where a node repre- E and F in the collaboration network (Figure 2) but there sents developer and relationships between developers, is no corresponding link between these two developers e.g., communication, coordination, are represented by an in the communication network (Figure 3). Developer E edge. and F are collaborating on the same part of source code Collaboration Network: A specific type of DSN which but they are not connected through any communication indicates the collaboration in a development community. link. Thus, this is considered as an instance of a missing Here, a node represents a developer who contributes to link between developer E and F. the project in the version control system. Two develop- ers are connected through an edge if they contribute to the same part of source code within a given time frame 3. Related Work [3]. Figure 2 represents an example of a collaboration In recent years, community smells are studied to incor- network. porate the organizational and social aspects of developer Communication Network: A specific type of DSN community in software engineering research. Some stud- which indicates the communication within the defined ies focused on defining different community smells that communication channel of a development community. can lead to unforeseen project costs [1], [6]. On the other Here, a node represents developers who communicate hand, some studies investigated the impact of community 65 8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020) zational Silo, Lone Wolf, Black Cloud and Radio Silence. They found that gender diverse team had a lower num- ber of community smells than non-gender diverse team. They also showed that gender diversity and women par- ticipation were important factors for Black Cloud and Radio Silence whereas organizational Silo and Lone wolf were found partially related. The existing studies have focused on community smells and the impact of these smells on software artifacts. The Figure 2: Collaboration Network phenomenon of community smells is surrounded with developers in a development community. However, devel- oper involvement in missing link smell and the relation between missing link smell and developer contributions have not been investigated yet. So, the developers in- volved with community smells and how their contribu- tion relate to missing link smell need to be explored. Figure 3: Communication Network 4. Methodology This study aims to understand how many developers of a project are involved in missing link smell. This study also smells on different software artifacts [2], [4]. wants to assess the relationship between a developer’s Tamburri et al. first introduced the concept of social contribution and involvement in missing link smell. First, debt in software engineering [6]. Later, in an industrial the missing link smell is detected for all the selected case study, they improved and elaborated the definition of projects. Then the percentage of smelly developers is social debt. In the same study, they defined nine different retrieved for each project. Later, the correlation analysis community smells which are connected to social debt is performed between a developer’s contribution and [1]. They also suggested a list of possible mitigations of involvement in missing link smell. community smells such as learning community, cultural conveyors, stand-up voting etc., to avoid the negative effects. 4.1. Dataset Magnoni proposed the identification pattern of four In this work, seven large open-source projects belonging out of nine community smells [3] defined in [1]. He to APACHE ecosystem are selected for analysis. These developed an open-source tool CODEFACE4SMELLS 1 as projects have been chosen because they are large and the an extension to CODEFACE [7]. This tool is capable of mailing lists are publicly available. Table 1 provides the detecting community smells from the change history list of analysed projects with their name, source code link, in the version control system and the communication development mailing list and analysis period. All projects history in development mailing list. are hosted in online version control system GitHub and Tamburri et al. analysed the distribution of community the development mailing list archives are available on smells in open-source projects [4]. They also assessed the Gmane 2 . relation between community smells and existing socio- The selected projects are large enough in terms of technical quality factors, e.g., socio-technical congruence, community members and the number of commits. The communicability, turnover etc. projects have 668 community members on average. All Palomba et. al examined the relationship between so- the projects have a substantial number of commits, with cial and technical debt [2], [8]. They assessed the impact an average of 10359. Thus the study has enough collabo- of community smells on code smells. They found commu- ration and communication data for analysis. nity smells significantly influencing code smell intensity. They also proposed a community-aware code smell in- tensity model in which both technical and community 4.2. Missing Link Smell Detection related factors were considered. The selected projects are analysed using a six-month anal- Catolino et al. analysed the role of gender diversity ysis window. The analysis period of a project starts from and women participation in community smell [5]. They when both communication in mailing list and change considered four types of community smell i.e., organi- history in repository are available. A few more months 1 2 https://github.com/maelstromdat/CodeFace4Smells http://gmane.io 66 8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020) Table 1 List of Analysed Projects # Project Name Source Code Mailing List Analysis Period 1 Apache Cassandra github.com/apache/cassandra gmane.comp.db.cassandra.devel Oct-2009 - Sep-2020 2 Apache Cayenne github.com/apache/cayenne gmane.comp.java.cayenne.devel Nov-2007 - Aug-2020 3 Apache CXF github.com/apache/cxf gmane.comp.apache.cxf.devel Nov-2010 - Sep-2020 4 Apache Jackrabbit github.com/apache/jackrabbit gmane.comp.apache.jackrabbit.devel Dec-2005 - Sep-2020 5 Apache Jena github.com/apache/jena gmane.comp.apache.jena.devel Oct-2012 - Sep-2020 6 Apache Mahout github.com/apache/mahout gmane.comp.apache.mahout.devel Oct-2008 - Aug-2020 7 Apache Pig github.com/apache/pig gmane.comp.java.hadoop.pig.devel Oct-2010 - Aug-2020 are excluded to make the analysis period divisible by six To calculate the percentage of smelly developers in a months. The analysis period for each project is given in project, the total number of developers of that project is Table 1. For example, Apache Cassandra project has the required. The total number of developers is defined as analysis period of 11 years starting from October 2009 to the sum of the number of developers who contribute to September 2020. source code and the number of members who communi- For every analysis window of a project, a communica- cate on mailing list [3]. The total number of developers tion network and a collaboration network is built. The of a project is obtained by counting the number of mem- communication network is generated by extracting com- bers present in either collaboration or communication munication data from development mailing list and the network generated by 𝐢𝑂𝐷𝐸𝐹 𝐴𝐢𝐸4𝑆𝑀𝐸𝐿𝐿𝑆. The per- collaboration network is generated by extracting collab- centage of smelly developers of a project is calculated oration data from the project repository. After having using the following formula (Equation 1), both communication and collaboration networks, the in- π‘›π‘’π‘šπ‘†π·π‘₯ stances of missing link smell are identified by comparing π‘π‘’π‘Ÿπ‘π‘†π·π‘₯ = Γ— 100%, (1) every collaboration link with communication networks. π‘‘π‘œπ‘‘π‘Žπ‘™π·π‘’π‘£π‘₯ If any collaboration link does not have its communica- where π‘›π‘’π‘šπ‘†π·π‘₯ is the number of smelly developers in tion counterpart, this link is identified as a missing link project π‘₯ and π‘‘π‘œπ‘‘π‘Žπ‘™π·π‘’π‘£π‘₯ is the number of total developers instance. in project π‘₯. An open-source tool, CODEFACE4SMELLS [4], is used to detect missing link community smell in this study. 4.4. Correlation Analysis This tool is capable of detecting missing link smell in the aforementioned way from project repository and RQ2 aims to understand the relationship between a de- development mailing list. The tool requires the link of veloper’s contribution and involvement in missing link source code repository and mailing list archive as input. smell. To address this RQ, the correlation between fol- Then the tool returns a list of missing link instances for lowing two measures is analysed: each window of the project. A missing link instance is 1. how many commits a developer has in the project represented by a pair of developers. For example, (π‘Ž, 𝑏) repository represents a missing link instance between developer π‘Ž 2. how many times a developer is involved in miss- and 𝑏. ing link smell In open-source projects, commits are the most representa- 4.3. Smelly Developers Identification tive form of coding contribution [9]. So, the contribution A developer involved with a missing link smell is consid- of a developer in a project is measured by the number ered as a smelly developer. An instance of missing link of commits of that developer in the project repository. smell consists of two collaborating developers who do The number of commits of every individual developer is not communicate with each other. Thus for every miss- retrieved from the source code repository. ing link smell, there are two smelly developers. CODE- The number of involvement in missing link smells can FACE4SMELLS outputs a missing link instance as a pair be obtained from the list of missing link instances of a of developers. So, the smelly developers can be obtained project. First, the developers are extracted from all the by extracting all missing link instances of a project. The missing link instances of the project. Then the number smelly developers of a project π‘₯ can be denoted by a set of involvement is calculated counting how many times a 𝑆𝐷π‘₯ . The number of smelly developers of the project will developer occurs in the list. be the number of elements in 𝑆𝐷π‘₯ . Both the number of commits and the number of in- volvement in smells of a developer are converted into 67 8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020) Table 2 Correlation coefficient interpretation Correlation Coefficient (Negative) Correlation Coefficient (Positive) Interpretation -0.4 < πœπ‘ ≀ 0.0 0.0 ≀ πœπ‘ < 0.4 Weak -0.7 < πœπ‘ ≀ -0.4 0.4 ≀ πœπ‘ < 0.7 Moderate -0.9 < πœπ‘ ≀ -0.7 0.7 ≀ πœπ‘ < 0.9 Strong -1.0 ≀ πœπ‘ ≀ -0.9 0.9 ≀ πœπ‘ ≀ 1.0 Very Strong percentage to achieve the relative measurement. The 5.1. RQ1: How many developers are commit percentage of a developer is calculated using involved in missing link community Equation 2. smell? To answer this RQ, all missing link smells of a project are π‘›π‘’π‘šπΆπ‘œπ‘šπ‘šπ‘–π‘‘π‘– π‘π‘’π‘Ÿπ‘π‘’π‘›π‘‘πΆπ‘œπ‘šπ‘šπ‘–π‘‘ = 𝑛 Γ— 100% (2) considered. For every project, the number of total devel- βˆ‘π‘–=1 π‘›π‘’π‘šπΆπ‘œπ‘šπ‘šπ‘–π‘‘π‘– opers and the number of smelly developers are calculated. Then the percentage of smelly developers is obtained for where π‘›π‘’π‘šπΆπ‘œπ‘šπ‘šπ‘–π‘‘π‘– is the number of commits of devel- each project. oper 𝑖 and 𝑛 is the total number of smelly developers. Table 3 demonstrates the percentage of smelly devel- Equation 3 is used to calculate missing link smell in- opers for each project. For example, Apache Cassandra volvement of a developer in percentage. project has 1380 total developers and 205 smelly devel- opers which is 14.9% of total developers. It is observed π‘›π‘’π‘šπ‘€π‘–π‘ π‘ π‘–π‘›π‘”πΏπ‘–π‘›π‘˜π‘– that on average 10.5% of total developers of a software π‘π‘’π‘Ÿπ‘π‘’π‘›π‘‘π‘€π‘–π‘ π‘ π‘–π‘›π‘”πΏπ‘–π‘›π‘˜ = 𝑛 Γ—100% (3) community are involved in missing link smells. Apache βˆ‘π‘–=1 π‘›π‘’π‘šπ‘€π‘–π‘ π‘ π‘–π‘›π‘”πΏπ‘–π‘›π‘˜π‘– Cayenne community has the highest percentage of smelly where π‘›π‘’π‘šπ‘€π‘–π‘ π‘ π‘–π‘›π‘”πΏπ‘–π‘›π‘˜π‘– is the number of involvement developers (21.1%). This is also the smallest community in missing link smells of developer 𝑖 and 𝑛 is the total among 7 communities. Tamburri et. al. found that the number of smelly developers. number of community smell grows quadratically with Finally, the correlation analysis is performed between the number of community members until the threshold π‘π‘’π‘Ÿπ‘π‘’π‘›π‘‘πΆπ‘œπ‘šπ‘šπ‘–π‘‘ and π‘π‘’π‘Ÿπ‘π‘’π‘›π‘‘π‘€π‘–π‘ π‘ π‘–π‘›π‘”πΏπ‘–π‘›π‘˜ for each project of 200 community members [4]. The occurrences of com- individually. Kendall’s tau-b [10] is used to assess the munity smell tend to stabilize after this threshold. As the degree of association between these two variables. Both number of total developers in Apache Cayenne commu- π‘π‘’π‘Ÿπ‘π‘’π‘›π‘‘πΆπ‘œπ‘šπ‘šπ‘–π‘‘ and π‘π‘’π‘Ÿπ‘π‘’π‘›π‘‘π‘€π‘–π‘ π‘ π‘–π‘›π‘”πΏπ‘–π‘›π‘˜ have tied values nity is less than 200, the number of missing link smell in the dataset. As Kendall’s tau-b can handle tied ranks, has not stabilized yet. So, this project has relatively more this is used for the correlation analysis. The correla- missing link smell and consequently more smelly devel- tion coefficient is considered significant if the p-value is opers. Excluding Apache Cayenne project, the rest six less than 0.01. The correlation coefficient is interpreted projects have 8.7% smelly developers on average. according to Table 2. The correlation coefficient, πœπ‘ , in- These results suggest that only a small portion of de- dicates the strength of the correlation. πœπ‘ has a range velopers in an open-source software community are in- of value from -1.0 to 1.0. As πœπ‘ closes to 0, it indicates volved with missing link smells. They do not commu- less correlation between two variables. As πœπ‘ approaches nicate appropriately with their co-committing or collab- to -1.0 or +1.0, the strength of correlation between two orative developers. Thus, they contribute to the total variables is increased. The positive value of πœπ‘ indicates a number of community smells in a software community. positive correlation and the negative value of πœπ‘ indicates a negative correlation between two variables. 5.2. RQ2: How does missing link smell relate with a developer contribution? 5. Result Analysis To answer this RQ, the correlation between a developer’s contribution and involvement in missing link smell is an- This section presents the result analysis and discussion alyzed. Kendall’s tau-b is used as a correlation technique of this study. All the missing link smells found in se- since it can handle tied values. lected projects are analysed to answer the two research First, the correlation analysis is performed individually questions. Analysis and discussion for both research for each development community. The Kendall’s tau-b questions are provided as follows. coefficients and p-values are provided in Table 4. For 68 8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020) Table 3 Percentage of Smelly Developers # Project Name Total Developers Smelly Developers Smelly Developers(%) Average 1 Apache Cassandra 1380 205 14.9% 2 Apache CXF 972 94 9.7% 3 Apache Jena 244 34 13.9% 8.7% 4 Apache Mahout 615 28 4.6% 5 Apache Pig 668 22 6.0% 6 Apache Jackrabbit 927 28 3.0% 7 Apache Cayenne 175 37 21.1% Average 668 64 10.5% Table 4 6. Threats to Validity Correlation Analysis This section discusses the potential threats that may af- # Project Name Tau-b p-value fect the validity of this study. 1 Apache Cassandra 0.508 < 0.01 Threats to external validity: Threats to external 2 Apache Cayenne 0.543 < 0.01 validity concern the generalization of the obtained results. 3 Apache CXF 0.528 < 0.01 In this study, seven projects from Apache are analysed. 4 Apache Jackrabbit 0.589 < 0.01 Thus the generalisation requires more projects belonging 5 Apache Jena 0.452 < 0.01 to different systems. However, to mitigate this threat 6 Apache Mahout 0.409 < 0.01 large and diverse projects are selected that have a long 7 Apache Pig 0.513 < 0.01 change history - 11 years on average. Overall 0.612 < 0.01 Threats to internal validity: Threats to internal va- lidity concern the factors that can influence the result but are not accounted for. In this study, CODEFACE4SMELLS example, the correlation coefficient for Apache Cassan- tool is used for the detection of missing link smell. The dra project is 0.508 and it represents a moderate positive outputs of CODEFACE4SMELLS are directly incorporated correlation. The value of correlation coefficient is sig- in this study without checking whether there is any de- nificant with a p-value less than 0.01. All seven projects fect in the tool. However, the capability of this tool of of this study show a moderate positive correlation be- identifying missing link smell was evaluated in [3]. This tween number of commits and number of smells which tool is also used in other studies in detecting community is statistically significant with p<0.01. smells [2], [5], [11]. Another correlation analysis is performed after com- Moreover, this tool relies on mailing list to detect bining the data from all the projects. The value of the communication among developers. But there may ex- correlation coefficient is slightly increased to 0.612 but ist other communication channels, e.g., Skype, Facebook still falls under the range of moderate positive correlation. etc., where developers communicate with each other. The This result is also statistically significant with a p-value result can be changed if these communication source are less than 0.01. considered. However, mailing list represents the main These results suggest that a developer who contributes communication channel for the projects analysed in this more in a project tends to have more missing link smells. study according to the contribution guidelines of these This can happen because a developer, who contributes projects. Besides, mailing list is used as the communica- more, have to communicate more with other develop- tion source in other related studies [4], [7]. ers. The overload of communication may be the reason for involving in more missing link smells than others. From another point of view, a developer having more 7. Conclusion contribution to a project is likely to be more familiar and This study explores the percentage of developers in a experienced with that project. As he knows most of the software development community involved in missing aspects of that project, he may take the communication link smells. Furthermore, the relationship between devel- with co-committers lightly while contributing. However oper contribution and involvement in missing link smell further analysis is required to find out the causes of in- is examined. At first, missing link smells are detected for volving in more smells. all the projects. Next, the smelly developers are identified 69 8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020) by extracting missing link instances. The percentage of and Human Aspects of Software Engineering, IEEE, smelly developers are calculated for every project. The 2013, pp. 93–96. number of appearances of a developer in missing link [7] M. Joblin, W. Mauerer, S. Apel, J. Siegmund, smell is counted. The contribution of a developer to a D. Riehle, From developer networks to verified project is measured by the number of commits. Finally, communities: a fine-grained approach, in: 2015 correlation analysis is done between contribution and IEEE/ACM 37th IEEE International Conference on involvement in smell. Software Engineering, volume 1, IEEE, 2015, pp. This study analyses seven open-source projects of 563–573. Apache. The result shows that the number of developers [8] F. Palomba, D. A. Tamburri, A. Serebrenik, A. Zaid- involved in missing link smells is 8.7% on average. This man, F. A. Fontana, R. Oliveto, Poster: How do study also founds that there is a moderate positive cor- community smells influence code smells?, in: 2018 relation between the number of commits of a developer IEEE/ACM 40th International Conference on Soft- and the number of involvement in missing link smells. ware Engineering: Companion, IEEE, 2018, pp. The developers who contribute more tend to involve in 240–241. more missing link smell. [9] S. Daniel, R. Agarwal, K. J. Stewart, The effects of di- In future, projects from other systems can be analysed versity in global, distributed collectives: A study of to assess the generalization of the result. Besides, other open source project success, Information Systems types of community smell, e.g., organizational silo, radio Research 24 (2013) 312–333. silence, can be examined to find their association with [10] M. G. Kendall, Rank correlation methods, 1948. developers contribution. [11] F. 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